{"id":"W4401153693","doi":"10.21037/tp-24-211","title":"Screening for biomarkers of tuberous sclerosis complex–associated epilepsy: a bioinformatics analysis","year":2024,"lang":"en","type":"article","venue":"Translational Pediatrics","topic":"Tuberous Sclerosis Complex Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Tuberous sclerosis; Medicine; Epilepsy; Bioinformatics; Pathophysiology; Neuroscience; Computational biology; Pathology; Psychiatry; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008104055,0.0002139577,0.0005482142,0.001357059,0.0001286124,0.00006783485,0.0001656271,0.0001336882,0.0002590057],"category_scores_gemma":[0.0002088484,0.000205757,0.0006727864,0.003895241,0.0001311226,0.0002218315,0.00002511117,0.000213718,0.0000161296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008011676,"about_ca_system_score_gemma":0.0002609736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007829851,"about_ca_topic_score_gemma":0.00004754379,"domain_scores_codex":[0.9974458,0.00005012125,0.0008123096,0.0003071734,0.000964014,0.0004206414],"domain_scores_gemma":[0.9978684,0.001198828,0.000116257,0.0002129883,0.0004158554,0.000187657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002382022,0.001789564,0.7487341,0.007369695,0.02396946,0.00003846104,0.004621298,0.01987395,0.01572934,0.0104677,0.03325318,0.1317713],"study_design_scores_gemma":[0.001838507,0.000260561,0.4817867,0.0001078107,0.003921716,0.000009764658,0.00008565795,0.5106657,0.0001047122,0.0004282301,0.000527639,0.000263029],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4650109,0.002397914,0.5225586,0.003452553,0.0001763538,0.001849775,0.002928578,0.0004410478,0.001184219],"genre_scores_gemma":[0.9351881,0.00009663329,0.0629857,0.00008952438,0.0001590915,0.00004073716,0.00131628,0.0000436985,0.00008021369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4907917,"threshold_uncertainty_score":0.8390529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1220126186329395,"score_gpt":0.3413165416069039,"score_spread":0.2193039229739644,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}